Kalman filters in process control systems

ABSTRACT

A control technique that enables the use of received process variable values in a Kalman filter based control scheme without the need to change the control algorithm includes a controller, such as a PID controller, and a Kalman filter, coupled to receive feedback in the form of, for example, process variable measurement signals from a process. The Kalman filter is configured to produce an estimate of the process variable value from slow or intermittent process feedback signals while providing a new process variable estimate to the controller during each of the controller execution cycles to enable the controller to produce a control signal used to control the process. The Kalman filter is also configured to compensate the process variable estimate for process noise with non-zero mean value that may be present in the process. The Kalman filter may apply this compensation to both continuously and intermittently received process variable values.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is related to U.S. application Ser. No. 13/782,478,entitled “Use of Predictors in Process Control Systems with Wireless orIntermittent Process Measurements” and filed on Mar. 1, 2013, the entiredisclosure of which is hereby expressly incorporated by referenceherein.

TECHNICAL FIELD

The present disclosure relates generally to process monitoring andcontrol systems and, more particularly, to the transmission andprocessing of continuous, wireless and/or intermittent controlcommunications in process control systems having controllers that useKalman filters.

BACKGROUND

Process control systems, such as distributed or scalable process controlsystems like those used in chemical, petroleum or other processes,typically include one or more process controllers communicativelycoupled to each other, to at least one host or operator workstation andto one or more field devices via analog, digital or combinedanalog/digital buses. The field devices, which may be, for example,valves, valve positioners, switches and transmitters (e.g., temperature,pressure and flow rate sensors), perform functions within the processsuch as opening or closing valves and measuring process parameters. Theprocess controller receives signals indicative of process measurementsmade by the field devices and/or other information pertaining to thefield devices, and uses this information to implement a control routineto generate control signals which are sent over the buses to the fielddevices to control the operation of the process. Information from thefield devices and the controller is typically made available to one ormore applications executed by the operator workstation to enable anoperator to perform any desired function regarding the process, such asviewing the current state of the process, modifying the operation of theprocess, etc.

Some process control systems, such as the DeltaV™ system sold by EmersonProcess Management, use function blocks or groups of function blocksreferred to as modules located in the controller or in different fielddevices to perform control and/or monitoring operations. In these cases,the controller or other device is capable of including and executing oneor more function blocks or modules, each of which receives inputs fromand/or provides outputs to other function blocks (either within the samedevice or within different devices), and performs some processoperation, such as measuring or detecting a process parameter,monitoring a device, controlling a device, or performing a controloperation, such as the implementation of aproportional-integral-derivative (PID) control routine. The differentfunction blocks and modules within a process control system aregenerally configured to communicate with each other (e.g., over a bus)to form one or more process control loops.

Process controllers are typically programmed to execute a differentalgorithm, sub-routine or control loop (which are all control routines)for each of a number of different loops defined for, or contained withina process, such as flow control loops, temperature control loops,pressure control loops, etc. Generally speaking, each such control loopincludes one or more input blocks, such as an analog input (AI) functionblock, a single-output control block, such as aproportional-integral-derivative (PID) or a fuzzy logic control functionblock, and an output block, such as an analog output (AO) functionblock. Control routines, and the function blocks that implement suchroutines, have been configured in accordance with a number of controltechniques, including PID control, fuzzy logic control, and model-basedtechniques such as a Smith predictor or Model Predictive Control (MPC).

To support the execution of the routines, a typical industrial orprocess plant has a centralized controller or room communicativelyconnected with one or more process controllers and process I/Osubsystems, which, in turn, are connected to one or more field devices.Traditionally, analog field devices have been connected to thecontroller by two-wire or four-wire current loops for both signaltransmission and the supply of power. An analog field device thattransmits a signal to the control room (e.g., a sensor or transmitter)modulates the current running through the current loop, such that thecurrent is proportional to the sensed process variable. On the otherhand, analog field devices that perform an action under control of thecontrol room is controlled by the magnitude of the current through theloop.

More recently, field devices have been designed operate to superimposedigital data on the current loop used to transmit the analog signals.For example, the Highway Addressable Remote Transducer (HART) protocoluses the loop current magnitude to send and receive analog signals, butalso superimposes a digital carrier signal on the current loop signal toenable two-way field communication with smart field instruments. Anotherprotocol generally referred to as the FOUNDATION® Fieldbus protocol isan all digital protocol, that actually defines two sub-protocols, onesupporting data transfers at a rate up to 31.25 kilobits per secondwhile powering field devices coupled to the network, and the othersupporting data transfers at a rate up to 2.5 megabits per secondwithout providing any power to field devices. With these types ofcommunication protocols, smart field devices, which may be all digitalin nature, support a number of maintenance modes and enhanced functionsnot provided by older control systems.

With the increased amount of data transfer, one particularly importantaspect of process control system design involves the manner in whichfield devices are communicatively coupled to each other, to controllersand to other systems or devices within a process control system or aprocess plant. In general, the various communication channels, links andpaths that enable the field devices to function within the processcontrol system are commonly collectively referred to as an input/output(I/O) communication network.

The communication network topology and physical connections or pathsused to implement an I/O communication network can have a substantialimpact on the robustness or integrity of field device communications,particularly when the network is subjected to adverse environmentalfactors or harsh conditions. These factors and conditions can compromisethe integrity of communications between one or more field devices,controllers, etc. The communications between the controllers and thefield devices are especially sensitive to any such disruptions, inasmuchas the monitoring applications or control routines typically requireperiodic updates of the process variables for each iteration of thecontrol routine or loop. Compromised control communications couldtherefore result in reduced process control system efficiency and/orprofitability, and excessive wear or damage to equipment, as well as anynumber of potentially harmful failures.

In the interest of assuring robust communications, I/O communicationnetworks used in process control systems have historically beenhardwired. Unfortunately, hardwired networks introduce a number ofcomplexities, challenges and limitations. For example, the quality ofhardwired networks may degrade over time. Moreover, hardwired I/Ocommunication networks are typically expensive to install, particularlyin cases where the I/O communication network is associated with a largeindustrial plant or facility distributed over a large area, for example,an oil refinery or a chemical plant consuming several acres of land. Therequisite long wiring runs typically involve substantial amounts oflabor, material and expense, and may introduce signal degradationarising from wiring impedances and electromagnetic interference. Forthese and other reasons, hardwired I/O communication networks aregenerally difficult to reconfigure, modify or update.

More recently, wireless I/O communication networks have been introducedinto the process control environment to alleviate some of thedifficulties associated with hardwired I/O networks. For example, U.S.Pat. No. 7,519,012, entitled “Distributed Control System for ControllingMaterial Flow Having Wireless Transceiver Connected to IndustrialProcess Control Field Device to Provide Redundant Wireless Access,” theentire disclosure of which is hereby expressly incorporated by referenceherein, discloses a system utilizing wireless communications betweencontrollers and field devices to, for example, augment or supplement theuse of hardwired communications. The use of wireless communicationsbetween devices within a process control network, such as betweencontrollers and field devices, has quickly gained momentum. In responseto this trend, various wireless communication protocols have beenestablished to support wireless communications within process plantenvironments, including the WirelessHART® protocol.

Complete reliance on wireless communications for control-relatedtransmissions has been limited however due to, among other things,reliability concerns. As described above, modern process controlmonitoring and control applications assume reliable data communicationsbetween the controller and the field devices to achieve optimum controllevels. Moreover, typical process controllers execute control algorithmsat fast rates to quickly correct unwanted deviations in the process andthese control algorithms rely on the availability of new processmeasurement data during each controller execution cycle. Undesirableenvironmental factors or other adverse conditions may createintermittent interferences that impede or prevent the fastcommunications necessary to support such execution of monitoring andcontrol algorithms.

Moreover, power consumption is sometimes a complicating factor for theimplementation of wireless communications in process controlenvironments. When disconnected from the hardwired I/O network, thefield devices may need to provide their own power source. Accordingly,wireless field devices are typically battery powered, draw solar power,or pilfer ambient energy such as vibration, heat, pressure, etc. Forthese types of devices, however, the energy consumed when performingdata transmission via a wireless network may constitute a significantportion of total energy consumption. In fact, more power may be consumedduring the effort to establish and maintain a wireless communicationconnection than during other important operations performed by the fielddevice, such sensing or detecting the process variable being measured.

Thus, the relatively recent introduction of wireless transmitters in theprocess control industry has presented many challenges when a wirelesslytransmitted measurement is to be used in closed loop control, becausethe process variable measurements provided in such systems are oftenreported on a much slower basis (e.g. 15 second update rate) than istypically provided by a wired transmitter. Also, the measurement valueprovided by a wireless device may be communicated on a non-periodicbasis. For example, windowed communications supported by someWirelessHART® devices may transmit new measurement values on anon-periodic basis. Still further, it is important that the loss ofcommunications in any wireless implementation be automatically treatedby the controller in a manner that does not introduce a processdisruption.

In response to these problems, methodologies have been developed toenable some process controllers, such asproportional-integral-derivative (PID) process controllers, to workeffectively with slow measurement updates, non-periodic measurementupdates and intermittent loss of communications, i.e., situations morefrequently associated with wireless communication networks. In general,these control methodologies receive and process unreliable ornon-periodically received feedback signals (e.g., intermittentlyreceived process variable measurements) while still controlling aprocess loop adequately. These systems thus enable, for example, a PIDcontroller to operate properly without receiving new process variablemeasurements for each execution cycle of the process control routine. Inparticular, U.S. Pat. Nos. 7,620,460; 7,587,252; and 7,945,339 and U.S.Patent Application Publication No. 2008/0082180, each of which isexpressly incorporated by reference herein, describe how, for example, aPID control routine may be modified to perform closed loop control usinga wireless transmitter that performs intermittent communications,thereby enabling process variable measurements to be communicated to thecontroller via a wireless communications link only when the processvariable changes a particular amount.

While effective, these new control techniques generally modify themanner in which a PID control routine or a PID control block handles theintermittently received process variable measurements at the input ofthe controller or the control routine. However, there are many types ofcontrol techniques, including some PID based control schemes, that useestimates of process variables as inputs to the controller instead ofusing measured process variable values and control signals as inputs tothe controller. For example, process control techniques that use Kalmanfilters typically operate on the measured process variable value toproduce an estimate of the process variable value, which is thenprovided to the control routine for generating a control signal. Inthese cases, the intermittently received process variable measurementssignals are not provided directly to the control routine. Still further,traditional Kalman filters assume the presence of zero-mean process ormeasurement noise within the process when producing an estimate of theprocess variable value. In processes in which this assumption is notcorrect, the Kalman filter based control system may operate poorly.

SUMMARY

A Kalman filter based control system or technique enables the use ofreceived process variable values from a process that exhibits non-zeromean process or measurement noise and/or process variable values thatare transmitted in a slow or intermittent manner without the need tochange the control algorithm. The control system includes a controller,such as a PID controller, and a Kalman filter coupled to receivefeedback in the form of, for example, intermittently measured or sentprocess variable measurement signals from a process, in which theprocess variable measurements may exhibit non-zero mean noise. TheKalman filter is configured to produce an estimate of the processvariable value from the received process feedback signals whileproviding a new process variable estimate to the controller to enablethe controller to produce a control signal used to control the process.

If desired, the Kalman filter may receive process variables in acontinuous or a slow or intermittent manner. In any case, the Kalmanfilter may use one or more process models to generate an estimate of areceived process variable to be provided at the input of the controllerfor use in producing a control signal for controlling the process, suchas for use in controlling the process variable being estimated.Continuously received process variable measurements may be communicatedto the Kalman filter via, for example, a wired communication network,while slow or intermittently received process variable measurements maybe communicated to the Kalman filter via, for example, a wirelesscommunication network. However, the slow or intermittently receivedprocess variable measurements could also be sent via a hardwired orother type of communication network if so desired. Moreover, while thecontroller may, for example, include and execute a PID controlalgorithm, the controller may execute or perform other desired types ofcontroller algorithms, such as MPC, neural network or other model ornon-model based control algorithms.

The Kalman filter may operate once during each of a number of executioncycles to produce a process variable estimate to be delivered to thecontroller, and may include a control signal input coupled to receivethe control signal produced by the control routine unit, an interfaceincluding a process variable feedback input that receives a processvariable measurement signal less frequently than once per the executioncycle time of the Kalman filter, and a process model coupled to receivethe control signal at the control signal input to produce an initialprocess variable estimate. The Kalman filter may also include acorrection unit coupled to use the process variable measurement signalreceived via the process variable feedback input to produce a correctionsignal from a residual, wherein the correction unit includes a firstcombiner, a switch unit and a gain unit. Still further, the Kalmanfilter may include a second combiner coupled to the process model and tothe correction unit to combine the initial process variable estimatewith the correction signal to produce a further process variableestimate. In this case, during the execution cycle of the Kalman filterunit at which a new value of the process variable measurement signal isavailable and during a predetermined number of execution cycles afterthe execution cycle at which a new value of the process variablemeasurement signal is available, the switch unit operates to provide anew value of the residual to the gain unit to produce the correctionsignal, wherein the new value of the residual is determined by combiningthe initial process variable estimate with a value of the processvariable measurement signal at the first combiner. However, duringexecution cycles of the Kalman filter unit after the predeterminednumber of execution cycles after the execution cycle at which a newvalue of the process variable measurement signal is available, theswitch unit operates to provide a stored value of the residual to thegain unit to produce the correction signal, wherein the stored value ofthe residual is determined during the last execution cycle of thepredetermined number of execution cycles. In one case, the predeterminednumber of execution cycles and in other cases the predetermined numberof execution cycles is greater than one.

When the process measurement signal is received in a slow, intermittentor otherwise non-periodic manner, the interface sets a flag or anothermarker to indicate the receipt of a new value of the process measurementsignal. Once the new value flag is set, and for a predetermined numberof execution cycles thereafter, the Kalman filter may calculate a newgain value, such as a new Kalman gain value, and a new residual value todevelop the correction signal. On the other hand, when the new valueflag is not set and after the predetermined number of execution cyclesthereafter, the Kalman filter may use a stored gain value and a storedresidual value to create the correction signal. The stored gain orresidual value is calculated during, for example, the last executioncycle of the predetermined number of execution cycles after the newvalue flag is set. In other words, after the initial execution cycle atwhich a new value of the process variable or process signal is availableat the Kalman filter input, the Kalman filter waits the predeterminednumber of execution cycles before calculating the gain or residual valuefor use during further execution cycles at which a new value of theprocess variable or process signal is not available.

Moreover, if the process includes process noise with a non-zero meanvalue, then an offset may exist between the process variable measurementand the process variable estimate. To remove this offset, the Kalmanfilter may include a compensation unit that produces a compensationsignal and another combiner that combines the compensation signal withthe process variable estimate to produce a compensated process variableestimate. In this regard, the Kalman filter provides a more accurateprocess variable estimate to the controller for use in process control,especially in process control that has non-zero mean value noise. Ingeneral, the Kalman filter may use the compensation unit for anycontinuous, wireless and/or intermittent process control communications.

In one embodiment, a control system for use in controlling a processincludes a control unit having a process variable input and a controlroutine unit communicatively coupled to the process variable input,wherein the control routine unit generates a control signal for use incontrolling the process based on a process variable value received atthe process variable input. The control system further includes a Kalmanfilter unit coupled to the control unit, the Kalman filter unitoperating once during each of a number of execution cycles to produce aprocess variable estimate. In this case, the Kalman filter unit includesa control signal input coupled to receive the control signal produced bythe control routine unit, an interface including a process variablefeedback input that receives a process variable measurement signal lessfrequently than once per the execution cycle time of the Kalman filterunit, and a process model coupled to receive the control signal at thecontrol signal input to produce an initial process variable estimate.The Kalman filter unit also includes a correction unit coupled to usethe process variable measurement signal received via the processvariable feedback input to produce a correction signal from a residual.The correction unit includes a first combiner, a switch unit and a gainunit. Further, a second combiner is coupled to the process model and tothe correction unit to combine the initial process variable estimatewith the correction signal to produce a further process variableestimate. During the execution cycle of the Kalman filter unit at whicha new value of the process variable measurement signal is available andduring a predetermined number of execution cycles after the executioncycle at which a new value of the process variable measurement signal isavailable, the switch unit operates to provide a new value of theresidual to the gain unit to produce the correction signal. The newvalue of the residual is determined by combining the initial processvariable estimate with a value of the process variable measurementsignal at the first combiner. However, during execution cycles of theKalman filter unit after the predetermined number of execution cyclesafter the execution cycle at which a new value of the process variablemeasurement signal is available, the switch unit operates to provide astored value of the residual to the gain unit to produce the correctionsignal, wherein the stored value of the residual is determined duringthe last execution cycle of the predetermined number of executioncycles. Still further, the process variable input of the control unit iscoupled to receive the process variable estimate based on the furtherprocess variable estimate to thereby operate to perform control of theprocess using this process variable estimate. If desired, the processvariable estimate is the further process variable estimate.

Furthermore, the gain unit may multiply a value of the residual by again value to produce the correction signal. As such, during theexecution cycle of the Kalman filter unit at which a new value of theprocess variable measurement signal is available and during thepredetermined number of execution cycles after the execution cycle atwhich a new value of the process variable measurement is available, thegain unit operates to determine a new gain value for use in the gainunit. However, during the execution cycles of the Kalman filter unitafter the predetermined number of execution cycles after the executioncycle at which a new value of the process variable measurement signal isavailable, the gain unit operates to use a stored gain value, whereinthe stored gain value is determined during the last execution cycle ofthe predetermined number of executions cycles.

Still further, the Kalman filter unit may include a compensation unitcoupled to use the residual to produce a compensation signal. Thecompensation unit may include a further gain unit that multiples a valueof the residual by a further gain value and a filter unit that receivesthe value of the residual multiplied by the further gain value toproduce the compensation signal. Additionally, the Kalman filter unitmay include a third combiner coupled to the second combiner and to thecompensation unit to combine the further process variable estimate withthe compensation signal to produce the process variable estimate tothereby operate to perform control of the process using this processvariable estimate. If desired, the control unit may store and execute oruse any desired type of control routine, and may, for example, store andimplement a proportional-integral-derivative control algorithm toproduce the control signal.

In another embodiment, a method of controlling a process includesimplementing, on a computer processor device, a control routine duringeach of a number of execution cycles to produce a control signal for usein controlling the process. This method receives, at a computerprocessor device, a process variable measurement signal less frequentlythan the execution cycle time rate and implements, on a computerprocessor device, a Kalman filter routine during each of the number ofexecution cycles to produce the process variable estimate. The methodimplemented to accomplish the Kalman filter routine includes receivingthe control signal produced by the control routine during each of thenumber of execution cycles, producing an initial process variableestimate using a process model to model the reaction of the process tothe control signal during each of the number of execution cycles, andproducing a correction signal from a residual during each of the numberof execution cycles. The Kalman filter routine method also combines,during each of the number of execution cycles, the initial processvariable estimate with the correction signal to produce a furtherprocess variable estimate. The Kalman filter routine method thenproduces a process variable estimate based on the further processvariable estimate. If desired, the process variable estimate is thefurther process variable estimate. In any event, during the executioncycle at which a newly received value of the process variablemeasurement signal is available and during a predetermined number ofexecution cycles after the execution cycle at which a newly receivedvalue of the process variable measurement is available, producing thecorrection signal from the residual includes using a new value of theresidual, wherein the new value of the residual is determined bycombining the initial process variable estimate with a received value ofthe process variable measurement signal. However, during executioncycles after the predetermined number of execution cycles after theexecution cycle at which a newly received value of the process variablemeasurement signal is available, producing the correction signal fromthe residual includes using a stored value of the residual, wherein thestored value of the residual is determined during the last executioncycle of the predetermined number of execution cycles. If desired, theKalman filter routine method also includes determining a compensationsignal and combining the further process variable estimate with thecompensation signal to produce the process variable estimate during eachof the number of execution cycles.

In still another embodiment, a Kalman filter that operates to produce acompensated process variable estimate includes an interface having aprocess variable feedback input that receives a process variablemeasurement signal. The Kalman filter also includes a control signalinput that receives a control signal, a process model coupled to receivethe control signal at the control signal input to produce an initialprocess variable estimate, and a correction unit coupled to use aprocess variable measurement signal received via the process variablefeedback input to produce a correction signal. In this case, thecorrection unit includes a first combiner that combines the initialprocess variable estimate with a received process variable measurementsignal to determine a residual and a gain unit that combines theresidual with a gain value to produce the correction signal. Further,the Kalman filter includes a second combiner coupled to the processmodel and to the correction unit that combines the initial processvariable estimate with the correction signal to produce a furtherprocess variable estimate. Still further, the Kalman filter includes acompensation unit coupled to use the residual to produce a compensationsignal. The compensation unit includes a further gain unit that combinesthe residual with a further gain value and a filter unit that receivesthe residual combined with the further gain value to produce thecompensation signal. Additionally, the Kalman filter includes a thirdcombiner coupled to the second combiner and to the compensation unit tocombine the further process variable estimate with the compensationsignal to produce the compensated process variable estimate.

If desired, the Kalman filter may operate at an execution cycle rate toproduce the compensated process variable estimate during each of anumber of execution cycles. In one case, the interface may receive theprocess variable measurement signal at a rate greater than or equal tothe execution cycle rate.

In another case, the interface may receive the process variablemeasurement signal at a rate less than the execution cycle rate. Here,the correction unit may further include a switch unit. As such, duringthe execution cycle of the Kalman filter at which a new value of theprocess variable measurement signal is available and during apredetermined number of execution cycles after the execution cycle atwhich a new value of the process variable measurement signal isavailable, the switch unit may operate to provide a new value of theresidual to the gain unit to produce the correction signal, wherein thenew value of the residual is determined by combining the initial processvariable estimate with a value of the process variable measurementsignal at the first combiner of the correction unit. However, duringexecution cycles of the Kalman filter after the predetermined number ofexecution cycles after the execution cycle at which a new value of theprocess variable measurement signal is available, the switch unit mayoperate to provide a stored value of the residual to the gain unit toproduce the correction signal, wherein the stored value of the residualis determined during the last execution cycle of the predeterminednumber of execution cycles.

In yet another embodiment, a Kalman filter adapted to execute on acomputer processor to produce a compensated process variable estimateincludes an interface routine stored in a non-transitory computerreadable medium and adapted to execute on the processor to receive aprocess variable measurement signal and to receive a control signal usedto control a process. The Kalman filter also includes a process modelingroutine stored in the non-transitory computer readable medium andadapted to execute on the processor to use the control signal to producean initial process variable estimate and includes a correction routinestored in the non-transitory computer readable medium and adapted toexecute on the processor to combine the initial process variableestimate with a received process variable measurement signal todetermine a residual. The correction routine combines the residual witha gain value to produce a correction signal. A combiner routine storedin the non-transitory computer readable medium and adapted to execute onthe processor combines the initial process variable estimate with thecorrection signal to produce a further process variable estimate.Further, the Kalman filter includes a compensation routine stored in thenon-transitory computer readable medium and adapted to execute on theprocessor to combine the residual with a further gain value and tofilter the residual combined with the further gain value to produce acompensation signal. A further combiner routine stored in thenon-transitory computer readable medium and adapted to execute on theprocessor combines the further process variable estimate with thecompensation signal to produce the compensated process variableestimate.

If desired, the Kalman filter may be adapted to execute on the processorat an execution cycle rate to produce the compensated process variableestimate during each of a number of execution cycles. In one case, theinterface routine may receive the process variable measurement signal ata rate greater than or equal to the execution cycle rate.

In another case, the interface routine may receive the process variablemeasurement signal at a rate less than the execution cycle rate. Here,the correction routine may further include a switching routine. As such,during the execution cycle of Kalman filter at which a new value of theprocess variable measurement signal is available and during apredetermined number of execution cycles after the execution cycle atwhich a new value of the process variable measurement signal isavailable, the switching routine operates to provide a new value of theresidual to be combined with the gain value to produce the correctionsignal. However, during execution cycles of the Kalman filter after thepredetermined number of execution cycles after the execution cycle atwhich a new value of the process variable measurement signal isavailable, the switching routine operates to provide a stored value ofthe residual to be combined with the gain value to produce thecorrection signal, wherein the stored value of the residual isdetermined during the last execution cycle of the predetermined numberof execution cycles.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of the disclosure, reference should bemade to the following detailed description and accompanying drawingfigures, in which like reference numerals identify like elements in thefigures, and in which:

FIG. 1 is a schematic representation of a process control system havinga controller configured to implement one or more control routines usingpredictors, such as Kalman filters, which may, in turn, usenon-periodically or intermittently received communications transmittedvia one or more slow or wireless communication networks;

FIG. 2 is a schematic representation of a known predictor based controlscheme that receives periodically generated process variable measurementsignals via a wired communication link, including a manner of modelingthe process being controlled;

FIG. 3 is a schematic representation of process control system that usesa process controller and a modified predictor to control a process, inwhich the modified predictor receives a process variable measurementsignal in a slow, non-periodic, or intermittent manner via a wirelesscommunication link;

FIG. 4 is a schematic representation of a known controller and a Kalmanfilter, wherein the Kalman filter is connected to a process to receiveperiodic measurements of a process variable via a wired communicationlink;

FIG. 5 is a schematic representation of a controller and a Kalmanfilter, used to control a process, wherein the Kalman filter isconfigured to receive periodic measurements of a process variable via awired communication link and to compensate for non-zero mean value noisein the process;

FIG. 6 is a schematic representation of a controller and a Kalmanfilter, used to control a process, wherein the Kalman filter isconfigured to receive non-periodic, intermittent or slow measurements ofa process variable via a wireless communication link;

FIG. 7 is a schematic representation of a controller and a Kalmanfilter, used to control a process, wherein the Kalman filter isconfigured to receive non-periodic, intermittent or slow measurements ofa process variable via a wireless communication link and to compensatefor non-zero mean value noise in the process;

FIG. 8 is a graph illustrating the modeled operation of a control systemhaving a PID controller with a modified Kalman filter that receivesnon-periodic, slow or intermittent process variable measurements via awireless communication link and compensates for non-zero mean valuenoise in the control system versus a control system having a PIDcontroller that periodically receives process measurements via a wiredcommunication link;

FIG. 9 is a schematic illustration of a process control system having acontroller block separate from a modified predictor block;

FIG. 10 is a schematic illustration of a process control system having amodified predictor block disposed in the same control module as acontroller block; and

FIG. 11 is a schematic illustration of a process control system having amodified Kalman filter integrated with a control algorithm block in acontroller.

While the disclosed system and method are susceptible of embodiments invarious forms, there are illustrated in the drawing (and will hereafterbe described) specific embodiments of the invention, with theunderstanding that the disclosure is intended to be illustrative, and isnot intended to limit the invention to the specific embodimentsdescribed and illustrated herein.

DETAILED DESCRIPTION

FIG. 1 depicts a process control system 10 that may be used to implementa control routine using predictor based control, and based on thereceipt of intermittent, slow or non-periodic process variablemeasurements, while still providing highly accurate control of aprocess. Generally speaking, the process control system 10 of FIG. 1includes a process controller 11 connected to a data historian 12 and toone or more host workstations or computers 13 (which may be any type ofpersonal computers, workstations, etc.), each having a display screen14. The controller 11 is also connected via hardwired communicationconnections to field devices 15-22 via input/output (I/O) cards 26 and28. The data historian 12 may be any desired type of data collectionunit having any desired type of memory and any desired or knownsoftware, hardware or firmware for storing data and, while beingillustrated as a separate device, may instead or in addition be part ofone of the workstations 13 or another computer device, such as a server.The controller 11, which may be, by way of example, a DeltaV™ controllersold by Emerson Process Management, is communicatively connected to thehost computers 13 and to the data historian 12 via a communicationnetwork 29 which may be, for example, an Ethernet connection.

The controller 11 is illustrated as being communicatively connected tothe field devices 15-22 using a hardwired communication scheme which mayinclude the use of any desired hardware, software and/or firmware toimplement hardwired communications, including, for example, standard4-20 mA communications, and/or any communications using any smartcommunication protocol such as the FOUNDATION® Fieldbus communicationprotocol, the HART® communication protocol, etc. The field devices 15-22may be any types of devices, such as sensors, valves, transmitters,positioners, etc., while the I/O cards 26 and 28 may be any types of I/Odevices conforming to any desired communication or controller protocol.In the embodiment illustrated in FIG. 1, the field devices 15-18 arestandard 4-20 mA devices that communicate over analog lines to the I/Ocard 26, while the field devices 19-22 are smart devices, such asFieldbus field devices, that communicate over a digital bus to the I/Ocard 28 using Fieldbus protocol communications. Of course, the fielddevices 15-22 may conform to any other desired standard(s) or protocols,including any standards or protocols developed in the future.

In addition, the process control system 10 includes a number of wirelessfield devices 60-64 and 71 disposed in the plant to be controlled. Thefield devices 60-64 are depicted in FIG. 1 as being transmitters (e.g.,process variable sensors) while the field device 71 is depicted as beinga valve. However, these field devices may be any other desired types ofdevices disposed within a process to implement physical controlactivities or to measure physical parameters within the process.Wireless communications may be established between the controller 11 andthe field devices 60-64 and 71 using any desired wireless communicationequipment, including hardware, software, firmware, or any combinationthereof now known or later developed. In the example case illustrated inFIG. 1, an antenna 65 is coupled to and is dedicated to perform wirelesscommunications for the transmitter 60, while a wireless router or othermodule 66 having an antenna 67 is coupled to collectively handlewireless communications for the transmitters 61-64. Likewise, an antenna72 is coupled to the valve 71 to perform wireless communications for thevalve 71. The field devices or associated hardware 60-64, 66 and 71 mayimplement protocol stack operations used by an appropriate wirelesscommunication protocols to receive, decode, route, encode and sendwireless signals via the antennas 65, 67 and 72 to implement wirelesscommunications between the controller 11 and the transmitters 60-64 andthe valve 71.

If desired, the transmitters 60-64 may constitute the sole link betweenvarious process sensors (transmitters) and the controller 11 and, assuch, are relied upon to send accurate signals to the controller 11 toensure that product quality and flow are not compromised. Thetransmitters 60-64, often referred to as process variable transmitters(PVTs), therefore may play a significant role in the control of theplant. Additionally, the valve or other field device 71 may providemeasurements made by sensors within the valve 71 or may provide otherdata generated by or computed by the valve 71 to the controller 11 aspart of the operation of the valve 71, including data collected by,computed by or otherwise generated by the function blocks FB1 and FB2executed within the valve 71. Of course, the valve 71 may also receivecontrol signals from the controller 11 to effect physical parameters,e.g., flow, within the plant.

The controller 11 is coupled to one or more I/O devices 73 and 74, eachconnected to a respective antenna 75 and 76, and these I/O devices andantennas 73-76 operate as transmitters/receivers to perform wirelesscommunications with the wireless field devices 61-64 and 71 via one ormore wireless communication networks. The wireless communicationsbetween the field devices (e.g., the transmitters 60-64 and valve 71)may be performed using one or more known wireless communicationprotocols, such as the WirelessHART® protocol, the Ember protocol, aWiFi protocol, an IEEE wireless standard, etc. Still further, the I/Odevices 73 and 74 may implement protocol stack operations used by thesecommunication protocols to receive, decode, route, encode and sendwireless signals via the antennas 75 and 76 to implement wirelesscommunications between the controller 11 and the transmitters 60-64 andthe valve 71.

As illustrated in FIG. 1, the controller 11 includes a processor 77 thatimplements or oversees one or more process control routines (or anymodule, block, or sub-routine thereof) stored in a memory 78. Theprocess control routines stored in the memory 78 may include or beassociated with control loops being implemented within the processplant. Generally speaking, the controller 11 executes one or morecontrol routines and communicates with the field devices 15-22, 60-64,and 71, the host computers 13 and the data historian 12 to control aprocess in any desired manner(s). However, it should be noted that anycontrol routines or modules described herein may have parts thereofimplemented or executed in a distributed fashion across multipledevices. As a result, a control routine or a module may have portionsimplemented by different controllers, field devices (e.g., smart fielddevices) or other devices or other control elements, if so desired.

Likewise, the control routines or modules described herein to beimplemented within the process control system 10 may take any form,including software, firmware, hardware, etc. Any device or elementinvolved in providing such functionality may be generally referred toherein as a “control element,” regardless of whether the software,firmware, or hardware associated therewith is disposed in a controller,a field device, or any other device (or collection of devices) withinthe process control system 10. Of course, a control module may be anypart or portion of a process control system including, for example, aroutine, a block or any element thereof, stored on any computer readablemedium. Such control modules, control routines or any portions thereofmay be implemented or executed by any element or device of the processcontrol system 10, referred to herein generally as a control element.Moreover, control routines, which may be modules or any part of acontrol procedure such as a subroutine, parts of a subroutine (such aslines of code), etc., may be implemented in any desired software format,such as object oriented programming, ladder logic, sequential functioncharts, function block diagrams, or using any other software programminglanguage or design paradigm. Likewise, the control routines may behard-coded into, for example, one or more EPROMs, EEPROMs, applicationspecific integrated circuits (ASICs), or any other hardware or firmwareelements. Still further, the control routines may be designed using anydesign tools, including graphical design tools or any other type ofsoftware/hardware/firmware programming or design tools. As a result, thecontroller 11 may be configured to implement a control strategy orcontrol routine in any desired manner.

In some embodiments, the controller 11 implements a control strategy orscheme using what are commonly referred to as function blocks, whereineach function block is an object or other part (e.g., a subroutine) ofan overall control routine that operates in conjunction with otherfunction blocks (via communications called links) to implement processcontrol loops within the process control system 10. Function blockstypically perform one of an input function, such as that associated witha transmitter, a sensor or other process parameter measurement device, acontrol function, such as that associated with a control routine thatperforms PID, fuzzy logic, etc. control, or an output function whichcontrols the operation of some device, such as a valve, to perform somephysical function within the process control system 10. Of course,hybrid and other types of function blocks exist and may be utilizedherein. The function blocks may be stored in and executed by thecontroller 11, which is typically the case when the function blocks areused for, or are associated with standard 4-20 mA devices and some typesof smart field devices such as HART® devices. Alternatively oradditionally, the function blocks may be stored in and implemented bythe field devices themselves, I/O devices, or other control elements ofthe process control system 10, which may be the case with systemsutilizing Fieldbus devices. While the description of the control system10 is generally provided herein using a function block control strategy,the disclosed techniques and system may also be implemented or designedusing other conventions or programming paradigms.

In any event, as illustrated by the exploded block 80 of FIG. 1, thecontroller 11 may include a number of control modules (illustrated asmodules 82, 84 and 86), wherein each control module implements one ormore process control loops. In this case, the control module 82implements an observer based control scheme or routine (which isreferred to herein as one type of predictor based control scheme), andthe control module 84 implements a predictor based control scheme. As anexample, the observer and/or predictor based control scheme may be basedon a Kalman filter. The modules 82 and 84 are illustrated as performingsingle loop control using an observer (OBS) or a predictor (PRD) and asingle-input/single output based PID control block (PID) connected toappropriate analog input (AI) and analog output (AO) function blocks,which may be associated with process control devices such as valves,with measurement devices such as temperature and pressure transmitters,or with any other device within the process control system 10. Amultiple input/multiple output control loop 86 is also illustrated asincluding an advanced control block 88 having inputs communicativelyconnected to one or more AI function blocks and outputs communicativelyconnected to one or more AO function blocks, although the inputs andoutputs of the advanced control block 88 may be connected to any otherdesired function blocks or control elements to receive other types ofinputs and to provide other types of control outputs. The advancedcontrol block 88 may implement any type of multiple-input,multiple-output control scheme, including observer or predictor basedcontrol, and may constitute or include a model predictive control (MPC)block, a neural network modeling or control block, a multi-variablefuzzy logic control block, a real-time-optimizer block, etc. It will beunderstood that the function blocks illustrated in FIG. 1 can beexecuted by the controller 11 or, alternatively, can be located in andexecuted by any other processing device or control element of theprocess control system, such as in one of the workstations 13 or in oneor more of the field devices 19-22 or 60-64 or 71.

The observer/predictor based control modules 82, 84 and 86 of FIG. 1have, in the past, generally been configured for periodic execution viamultiple iterations of the control routine. In a conventional case, eachiteration is supported by an updated process measurement provided by,for instance, a transmitter or other field device. There are typicallymultiple process measurements made between each of the periodicexecution iterations to assure an up-to-date process measurement duringeach controller execution cycle. In fact, to avoid the restrictions ofsynchronizing the measurement value with the control, many pastcontrollers (or control loops) were designed to over-sample themeasurement by a factor of 2-10 times. Such over-sampling helped toensure that the process measurement was current for use in the controlscheme. Also, to minimize control variation, conventional designsspecified that feedback control should be executed 4-10 times fasterthan the process response time. To satisfy these conventional designrequirements, the measurement value had to be sampled much faster thanthe control execution rate, which was much faster or higher than theprocess response time.

Generally speaking, the observer/predictor based control modules of FIG.1 (which will be described in greater detail herein) enable thetransmission of the process variable measurement values (which includesother values such as computed or simulated values) at much lower rates,at intermittent rates or at non-periodic rates, so as to, for example,allow control in processes in which only intermittent process variablefeedback communications exist or to reduce the consumption of power fromthe power supply powering, for example, a wireless sensor or transmitter(e.g., one of the transmitters 60-64) making the process variablemeasurements. In this later case, even if measurement and controlexecution are synchronized, as is the case in many Fieldbus controlschemes, the conventional approach to scheduling control iterations 4-10times faster than the process response may still result in too muchpower consumption during data transmission. Additionally, theobserver/predictor control routines may perform observer based controlin the presence of non-zero mean process or measurement noise within themeasured process variable used as an input to the observer of theobserver based control routine.

As will be discussed in more detail below, the predictor based controltechniques described herein are particularly useful in the processcontrol system 10, and in particular in the controller 11 and in thetransmitting devices and other field devices of the control system 10,when these devices transmit or receive a new measurement or other valueon a non-periodic or an intermittent basis, such as when certainconditions are satisfied. For example, a new measurement value may betransmitted based on whether the process variable has changed by morethan a predetermined threshold (e.g., an amount determined to besignificant). For example, if the magnitude of the difference betweenthe new measurement value and the last communicated measurement value isgreater than a specified resolution, then a trigger may be generatedsuch that the measurement will be updated or sent. When dealing withdiscrete measurements (such as on/off measurements, a digital bit orother state measurements in which one of a predetermined set of statesor discrete values is expected or measured), a change from one state toanother is generally considered to exceed the threshold or resolutionmagnitude.

In other cases, a new measurement value may be transmitted when thedifference exceeds the specified resolution (as in the prior case), aswell as when the time since the last communication exceeds apredetermined refresh time. Here, either a change in the processvariable or the passing of a default time, may result in a measurementtransmission. The refresh, or default, time for measurement transmissionmay vary between control loops, inasmuch more or less frequent updatesmay be suitable depending on whether the process is slow moving or rapidin response (as indicated, for instance, by the process time constant).In some cases, a determination may be made during the tuning of thecontrol loop based on the time constant, and adjusted thereafter asdesired. For example, the time between measurements or sending ofsignals may be dependent on the measured state of the variable or valueand, in this case, the period of measurement can be adjusted to reflectthe state of the device, equipment, or process that is being monitored.In any case, the default or refresh time acts as an integrity check, oroverride, after periods of time without a measurement update. Suchchecks may be useful to, for instance, facilitate the final drive of theprocess variable to a target.

In the meantime, the transmitter, sensor or other field deviceresponsible for obtaining the measurement values may still periodicallysample the measurement at any desired rate, such as the conventional4-10 times the process response time. The communication techniques thendetermine whether the sampled values are transmitted to the controller11.

However, the underlying assumption in standard control design (e.g.,using z transform, difference equations, etc.) and digitalimplementation of the control routines, such asproportional-integral-derivative (PID) control, is that the controlalgorithm is executed on a periodic basis. If the measurement is notupdated during each execution cycle, then steps such as the integral (orreset) portion or contribution of the routine may not be appropriate.For example, if the control algorithm continues to execute using thelast, outdated measurement value, then the output will continue to movebased on the reset tuning and the error between the last measured valueand the set point. On the other hand, if the control routine is onlyexecuted when a new measurement is communicated, then the controlresponse to setpoint changes and feedforward action on measureddisturbances could be delayed. Control routines may also includecalculations based on the time elapsed since the last iteration.However, with non-periodic and/or less frequent measurementtransmissions, calculating the reset contribution based on the controlexecution period (i.e., the time since the last iteration) may result inincreased process variability.

In view of the foregoing challenges, and to provide accurate andresponsive control when process variable measurement values are notupdated on a periodic basis, control techniques may be used thatgenerally modify the process control routine based on whether an updateof the process variable is available. In some cases, a predictor basedcontrol routine may be restructured in accordance with the techniquesdescribed herein based on the expected process response since the lastmeasurement update.

While the control techniques described below are useful in, and areespecially applicable to control routines that implement communicationsvia wireless communication networks, it should be noted that thesetechniques are applicable to communications implemented via hardwiredconnections as well. For example, one or more of the hardwired devices15-22 may also rely on a limited power supply or otherwise benefit fromreduced data transmission rates. In addition, the process control system10 may include a sampled analyzer or other sampling system designed toprovide measurement data via hardwired communication networksintermittently or at rates slower than the control execution rate.

Further, the control techniques described below are adapted to eliminateany offset between the process variable measurement values and processvariable estimate values caused by the presence of process noise withnon-zero mean value. This compensation for non-zero mean value noise maybe performed regardless of whether the control techniques areimplemented via wired or wireless communication networks.

For the sake of illustration, FIG. 2 depicts a typical prior art,predictor based control system 100 having a controller 101 connected toa process 102, and including a predictor 104 connected between thecontroller 101 and the process 102. As illustrated in FIG. 2, thecontroller 101, which may be for example, a PID controller (whichincludes any of P, PI, PD, ID and PID type controllers) produces acontrol signal U_(j) that controls the operation of some device, such asa valve, within the process 102 to affect or change the value of acontrolled process state variable X_(j). Moreover, a transmitter 106measures or samples a process variable effected by the control operationto produce process output values Z_(j). In this case, the process outputvalues Z_(j) may be measured values of the process state variable X_(j)or may be measured values of some other process variable that isassociated with or that changes in some known relationship to theprocess state variable X_(j). The transmitter 106 provides themeasurement output values Z_(j) to the predictor 104 which may be anobserver, as an example. In this case, the transmitter 106 isillustrated as being a wired transmitter and so communicates measuredprocess output values Z_(j) to the predictor 104 via a wiredcommunication network. Also, as noted above, the transmitter 106typically measures and sends new process output values Z_(j) at a rate4-10 times faster than the execution rate of the controller 101.

As illustrated in FIG. 2, the predictor 104, which may be for example, aKalman filter uses the received measured process output values Z_(j) inconjunction with the control signal U_(j) produced by the controller 101to develop an estimate of the process state variable {circumflex over(X)}_(j) which is then provided as an input to the controller 101 foruse in controlling the process 102 and, in particular, to control thevalue of the process state variable X_(j). The use of the predictor 104enables the process control system 10 to account for such things asmeasurement delay, measurement errors, process noise, etc., to enablethe controller 101 to perform better or more accurate control of theprocess 102. It should be noted that the predictor 104 typicallyoperates on the same or a faster execution cycle than the controller 101so that a new valve of the process state variable estimate {circumflexover (X)}_(j) available at the input of the controller 101 at eachexecution cycle of the controller 101. As such, it is typical to providea new process measurement output value Z_(j) to the input of thepredictor 104 at a rate that is equal to or greater (e.g., 4-10 timesgreater) than the execution rate of the predictor 104.

Generally speaking, a predictor, such as the predictor 104 of FIG. 2,assists in process control by providing a process variable estimateusing a model of the process. For example, many industrial process unitsare characterized by one manipulated input, U(t), and one measuredprocess output, Z(t). The model of a liner processes with onemanipulated input and one measured process output may be expressed instate variable format as:X _(j) =aX _(j-1) +bU _(j)Z _(j) =hX _(j)

wherein:

X_(j)=process state at time j;

U_(j)=process input at time j;

Z_(j)=process output measurement at time j;

a and b=constants defining the process gain and dynamic response; and

h=process output gain/unit conversion gain.

For example, the state variable representation of a first order processmay be expressed in this format where:

${a = {\mathbb{e}}^{\frac{{- \Delta}\; T}{\tau}}},{b = {k\left( {1 - {\mathbb{e}}^{\frac{{- \Delta}\; T}{\tau}}} \right)}}$wherein:

k=process gain;

τ=process time constant;

ΔT=period of execution of process model; and

j=current time instance.

For an integrating process, the state variable representation of a firstorder process may be expressed in this format where:

a=1; and

b=ΔT*k₁.

To aid in the discussion, the process 102 depicted in FIG. 2 isillustrated in a mathematical form provided above, to illustrate thevarious sources of errors and other operations inherent in mostprocesses, and to enable a better understanding of the predictor 104. Inparticular, the process 102 is modeled by process gain block 120, asummer 122, a conversion block 124, a summer 126 and a feedback loop 130including a delay unit 132 and a gain unit 134. To depict themathematical operation of the process 102, the gain unit 120 multipliesthe control signal U_(j) produced by controller 101 by a process gain b,and this value is added (in the summer 122) to process noise W_(j) and adynamic response that is estimated by or modeled by the feedback loop130 to produce the process state variable X_(j). The process noise W_(j)may be assumed to be, for example, Gaussian white noise, or zero meanwhite noise with covariance Q that is uncorrelated with the input,although other types of noise models may be used instead. Thus, theprocess state variable X_(j) is mathematically modeled as the sum ofoutput of the process gain block 120, the process noise W_(j) and thedynamic gain response. In this case, the dynamic gain response at theinput of the summer 122 is produced by or modeled by the feedback loop130, in which the process state variable X_(j) is delayed by onesampling or controller execution time in the delay unit 132, and thedelayed version of the process state variable X_(j-1) is multiplied bythe process dynamic response gain a. As will be understood, the processgain b and the dynamic process response gain a are typically modeled asconstants, but may be updated (periodically or otherwise) based on theactual operation of the process 102.

Within the process 102, the process state variable X_(j) is illustratedas being converted in the conversion unit 124 by being multiplied by thevalue h, which provides for or models the unit conversion between theunits of the process state variable X_(j) and the measured processoutput value Z_(j). The summer 126 then sums the converted process statevariable value produced by the block 124 with a value V_(j) whichrepresents the process measurement noise (e.g., the noise added to theprocess variable value due to measurement errors or inaccuracies). Ifdesired, the measurement noise V_(j) may be white noise with zero meanand covariance R that is uncorrelated with the input or the processnoise W_(j). The output of the summer 126 represents the process outputvalue Z_(j) which is an estimate of the value of the process statevariable X_(j) including the effects of process noise, process dynamics,process gain and having errors therein due to measurement noise. Ofcourse, as illustrated in FIG. 2, the transmitter 106 produces ormeasures the “noisy” process output value Z_(j) and provides this valueto the predictor 104.

As noted above, the transmitter 106 will typically produce a processmeasurement output value Z_(j) multiple times per operation cycle of thecontroller 101 and/or per operational cycle of the predictor 104, so asto enable the predictor 104 to produce a valid and up-to-date estimate{circumflex over (X)}_(j) of the actual process state variable X_(j)based on the process output measurements Z_(j) provided thereto by thetransmitter 106.

FIG. 3 illustrates a new process control system 200 including a processcontroller 201 (which may be the same as the process controller 101 FIG.2) that controls a process 202. As illustrated in FIG. 3, the processcontroller 201 includes a controller algorithm, such as PID controlleralgorithm 230, which produces a control signal U_(j) to control or drivethe process 202. In this case, however, the control system 200 includesa modified predictor 204 connected to the controller 201 and to atransmitter 206. The modified predictor 204 receives an output of thetransmitter 206 which, in this case, is provided wirelessly to themodified predictor 204 via a wireless communication network (indicatedby the dotted line in FIG. 3). The modified predictor 204 operates toproduce an estimated (predictor or observed) value of the process statevariable {circumflex over (X)}_(j) on the control signal U_(j) and themeasured process output value Z_(j) as measured by transmitter 206, andprovides the estimated process state variable {circumflex over (X)}_(j)to the input of the PID controller algorithm 230 for use in producingthe control signal U_(j).

Here, the output of the wireless transmitter 206 (i.e., the processmeasurement output signal Z_(j)) is provided wirelessly over a wirelesscommunication network to the modified predictor 204 and, as such, may beprovided non-periodically, intermittently, or at a rate that is slowerthan the execution cycle rate of the controller 201 or the executioncycle rate of the predictor 204. As a result, in this case, a new valueof the measured process output Z_(j) may not be, and is generally notavailable at the input of the predictor 204 at the start of each newexecution cycle of the predictor 204. Nonetheless, the modifiedpredictor 204 still operates in manners described below to produce a newprocess state variable estimate {circumflex over (X)}_(j) to the inputof the controller 201 or the control routine 230 during each controllerexecution cycle.

As noted above, in the past, observer/predictor based controlleralgorithms, such as PID algorithms, tied to predictors such as Kalmanfilters assumed that the process variable measurements were up-to-dateand, in fact, accurate at the start of each controller execution cycle.These algorithms also assumed that the measured process variable (thesame variable being estimated by the Kalman filter) includes onlynon-zero mean noise. As a result, the use of a wireless transmitterother mechanism that provides slow or intermittent process variablemeasurement values to a typical observer/predictor may cause problemsand lead to poor control performance. The modified predictor 204,however operates in manners described below to minimize or alleviate theissues associated with the receipt of intermittent or slow processfeedback signals at the controller or observer/predictor inputs.

Before describing the specifics of the modified predictor 204 of FIG. 3,it will be helpful to describe the typical operation of one known typeof predictor, in the form of a Kalman filter, which is generally apredictor that corrects for model inaccuracies during operation of theprediction unit. FIG. 4 illustrates a typical predictor based controlsystem 400 having a controller 401 coupled to a process 402 that uses aKalman filter 404 that receives periodic process feedback signals from awired transmitter 406. For the sake of this discussion, the process 402is assumed to operate in the same manner as described for the process102 of FIG. 2, and thus the blocks illustrated in the process 402 ofFIG. 4 are numbered the same as corresponding blocks in the process 102of FIG. 2.

Generally speaking, a Kalman filter is typically used to control aprocess when the process is characterized by substantial process ormeasurement noise, as the Kalman filter can be used to reduce the impactof process or measurement noise on a control application. In particular,the Kalman filter generally produces an estimated process output forcontrol, as will be described in more detail below, wherein:

-   -   {circumflex over (X)}_(j) ⁻=the a priori estimate of the process        state (an initial process state variable estimate);

{circumflex over (X)}_(i)=estimate of the process state; and

{circumflex over (Z)}_(j)=estimate of the output.

In a general sense, a Kalman filter includes and uses a model of theprocess without process or measurement noise to produce an estimatedprocess output value. The Kalman filter also includes a correction unitthat determines the difference between the estimated process output andthe measured process output as a residual, also known in the art as aninnovation. The correction unit of the Kalman filter also includes aKalman gain, K_(j), which is set to determine what portion of theresidual is used in the Kalman filter model to compensate forinaccuracies in the process model variables a, b, or h, and to accountfor the process or measurement noise.

Thus, as illustrated in FIG. 4, a typical Kalman filter 404 receives ameasured process output Z_(j), as measured by the wired transmitter orsensor 406, via hardwired or regularly scheduled communications, andproduces an estimate of the process state variable {circumflex over(X)}_(j) an output, which is, in turn, provided as a process variableinput to the controller 401 (illustrated in FIG. 4 as a PID controlleralgorithm 430 being implemented by the controller 401.) The Kalmanfilter 404 includes a gain block 410, summers 412 and 414, a conversionblock 416, a further summer 418, a Kalman gain block 422 coupled betweenthe summer 418 and the summer 414, and a dynamic feedback loop 423including a delay unit 424 and a gain block 426. Essentially, the blocks410, 412, 424 and 426 form a process model that produces the initialprocess state variable estimate {circumflex over (X)}_(j) ⁻, the blocks416, 418 and 422 form a correction unit that produces a correctionsignal at the output of the gain block 422 and the block 414 forms acombiner that combines the initial process state variable estimate{circumflex over (X)}_(j) ⁻ with the correction signal to form theprocess state variable estimate {circumflex over (X)}_(j). As a result,the Kalman filter 404 is configured to produce an estimated non-noisyvalue for the process state variable {circumflex over (X)}_(j) (which isan estimate of the process state variable X_(j) within the process 402)based on a process gain b, a dynamic response gain a, and a Kalmanfilter gain K_(j), as well as the current values of the control signalU_(j) and the measured process output signal Z_(j).

In particular, the gain unit 410 multiplies the control signal U_(j)(from the controller 401) by an estimate of the process gain b, and thisvalue is added in the summer 412 to a dynamic response (produced by thefeedback loop 423) to produce an a priori estimated value of the processstate variable, indicated as the variable {circumflex over (X)}_(j) ⁻.The dynamic gain response provided at the input of the summer 412 isproduced by the feedback loop 423, in which the estimated process statevariable {circumflex over (X)}_(j) is delayed by one sampling orcontroller execution time in the delay unit 424, and the delayed versionof the process state variable {circumflex over (X)}_(j-1) is multipliedby the dynamic process response gain a. As will be understood, theprocess gain b and the dynamic process response gain a are typicallymodeled as constants, but may be updated (periodically or otherwise)based on the actual operation of the process 402. In any event, the apriori process state variable estimate {circumflex over (X)}_(j) ⁻ isthen provided to the summer 414 which sums this variable value with acorrection signal that corrects for the noise and model inaccuracies inthe process model to produce the process state variable estimate{circumflex over (X)}_(j) (also referred to as the observed processstate variable value). In this case, the blocks 416, 418 and 422 producethe correction signal.

To produce the correction signal at the input of the summer 414, the apriori estimated process state variable {circumflex over (X)}_(j) ⁻ isconverted in the conversion unit 416 by being multiplied by the value h,which provides for or models the units conversion between the units ofthe process state variable {circumflex over (X)}_(j) ⁻ and the measuredprocess output Z_(j) to produce what is still considered to be theinitial process state variable estimate in the form of the estimatedoutput value {circumflex over (Z)}_(j). The summer 418 then subtracts(computes the difference between) the initial estimated value of themeasured process output {circumflex over (Z)}_(j) from the actualmeasured value of the process output Z_(j) to produce a residual on aline 440. The residual is then multiplied in the block 422 by the Kalmanfilter gain K_(j) to produce the correction signal for correcting forthe noise in the process (e.g., the process measurement noise and theprocess noise) as well as to correct for model inaccuracies, such asinaccuracies in the gains a, b and h. Ultimately, the estimated processstate variable {circumflex over (X)}_(j) is provided as the input to thecontroller 401 for use in controlling the process 402. The Kalman gainK_(j) and other variables of the Kalman filter 404 are typicallygenerated once, or are regenerated during each execution cycle todetermine proper control and to tweak the observer 404 to be moreaccurate.

More particularly, the Kalman gain K_(j) may be assumed to be a constantif the process is noise free and the values of a, b, and h areaccurately known. However, as is known, an optimal linear estimator maybe achieved by dynamically calculating the Kalman gain K_(j) in arecursive manner during each execution cycle of the Kalman filter in thefollowing manner. First, an initial guess or estimate of the previousvalues may be established as:{circumflex over (X)} _(j-1)=a posteriori state estimateP _(j-1)=a posteriori state covariance

Then, in a predictor step, the following calculations may be made todetermine the current values of the a priori estimate of the processstate variable {circumflex over (X)}_(j) ⁻ and the a priori estimate ofthe process noise covariance P_(j) ⁻ as:{circumflex over (X)} _(j) ⁻ =a{circumflex over (X)} _(j-1) +bU _(j)P _(j) ⁻ =a ² P _(j-1) +Q

Then, in an estimation step, the following calculations may be performedto determine a new value for the Kalman gain, the process state variableestimate {circumflex over (X)}_(j), the state covariance P_(j) and theestimated measured process output value Z_(j):

$K_{j} = \frac{{hP}_{j}^{-}}{{h^{2}P_{j}^{-}} + R}$X̂_(j) = X̂_(j)⁻ + K_(j)(Z_(j) − hX̂_(j)⁻) P_(j) = P_(j)⁻(1 − hK_(j))Ẑ_(j) = hX_(j)⁻

wherein:

K_(j) is the Kalman gain;

R is the covariance of the measurement noise (V_(j)); and

Q is the covariance of the process noise (W_(j)).

After the initial run, the predictor step and the estimator step can berepeated each time the Kalman gain is recalculated, e.g., during eachexecution cycle.

Thus, as will be understood, the Kalman filter 404 of FIG. 4 attempts toestimate the actual value of the process state variable X_(j),accounting for the process noise and measurement noise, for example,added by the transmitter or sensor 406, and provides this estimate ofthe process state variable {circumflex over (X)}_(j), to the controller401 as an input. Ultimately, the use of the Kalman filter 404, providesa better or more true estimate of the actual process state variableX_(j) at the controller input for use in controlling the process 402, asopposed to using the measured or sensed value of the process outputZ_(j) as measured by the transmitter 406.

The Kalman filter of FIG. 4 works well when the process noise is assumedto have a zero mean value. However, when the mean value of the processnoise is not zero (0), then for values of the Kalman gain K_(j) that areless than 1/h, there will exist an offset between the measurement andestimate of the state. Thus, for many practical applications of a Kalmanfilter, modifications are needed in the Kalman filter to account fornon-zero mean value noise.

FIG. 5 illustrates a Kalman filter based control system 500 having acontroller 501 used to control a process 502, and a Kalman filter 504that is modified to account for process noise with non-zero mean value.The Kalman filter 504 of FIG. 5 is essentially the same as that of FIG.4 (and so like elements are numbered the same), except with the additionof a gain block 522, a filter block 524, and a summer 526. The blocks522 and 524 form a compensation unit that produces a compensation signalat the output of the filter block 524 and the block 526 forms a combinerthat combines the process state variable estimate {circumflex over(X)}_(j) with the compensation signal to form a compensated processstate variable estimate {tilde over (X)}_(j). As a result, the Kalmanfilter 504 is configured to eliminate the offset caused by the processnoise with non-zero mean value to produce an offset-free estimatednon-noisy value of the process state variable.

To produce the compensation signal at the input of the summer 526, thecomputed residual from the summer 418 is first multiplied in the gainblock 522 by one minus the Kalman gain K_(j) and then fed to the filterblock 524. The filter block 524 may be, for example, any type of firstorder filter, such as a low-pass filter, with a time constant that isapproximately equal to the process response time (e.g., the process timeconstant plus any process dead time). The filter block 524 estimates thenon-zero mean value of the process noise and provides, as an output, thenecessary compensation signal to eliminate the offset caused by thenon-zero mean value noise. The generated compensation signal is thencombined with the estimated process state variable {circumflex over(X)}_(j) to remove the noise offset inherent in the {circumflex over(X)}_(j) value, which was calculated based on the measured processoutput Z_(j) which includes the effects of the non-zero mean valuenoise. In this manner, the Kalman filter 504 of FIG. 5 accounts for anyprocess noise having a non-zero mean value within the process 502 inorder to provide a more accurate process state variable estimate to thecontroller 501. Of course, if the process noise in the process 502 has azero mean value, then the compensation signal at the output of thefilter block 524 will be zero or approximately zero.

Still further, the Kalman filters 404 of FIGS. 4 and 504 of FIG. 5assume that a new measured output value Z_(j) is available and providedby the transmitter 406 during every execution cycle of the Kalman filter404 to enable the Kalman filter 404 or 504 to determine and to provide anew process variable estimate {circumflex over (X)}_(j) to thecontroller 401 or a compensated process state estimate {circumflex over(X)}_(j) to the controller 501 at the beginning of each controlexecution cycle. However, when using a wireless transmitter or othersystem in which the measured signals are not sent to the Kalman filterat the same or greater rate than the execution rate of the controller401 or 501, the use of the Kalman filter as illustrated in FIGS. 4 and 5is not possible because new sample values are not available at eachexecution cycle.

FIG. 6 illustrates a modified predictor based process control system600, such as that generally illustrated in FIG. 3, in which a processcontroller 601 controls a process 602 which may be the same as theprocess 402 of FIG. 4. In this case, the modified predictor 204 of FIG.3 is illustrated in FIG. 6 as a modified Kalman filter 604. The Kalmanfilter 604 is similar to the Kalman filter 404 of FIG. 4 except that itis adapted to deal with the receipt of intermittent, slow ornon-periodic process measurements made in the process 602 or processmeasurements that otherwise arrive at a rate substantially slower thanor less than the execution cycle rate of the Kalman filter 604 or thecontroller 601. In this case, the Kalman filter 604 of FIG. 6 includesthe basic elements illustrated FIG. 4 (in which the same elementsinclude the same reference numbers) and thus is generally adapted to beused for processes in which there is no significant process dead time orprocess response delay.

As denoted in FIG. 6, however, the modified Kalman filter 604 includesan input interface 660, which accepts or receives the processmeasurement output signals Z_(j) sent by a wireless transmitter 606(which may the same as the wireless transmitter 206 of FIG. 3). Thereceipt of the control signal at the Kalman filter 604 may also beaccomplished via this interface. Here it is assumed that the wirelesstransmitter 606 sends process measurement signals intermittently,non-periodically and/or at a rate that is slower than the execution rateof the Kalman filter 604. The transmitter 606 could, for example, send aprocess measurement signal only at times that the process measurementsignal changes a preset amount from the last sent process measurementsignal, or could send a process measurement signal at a periodic ratethat is less than the execution rate of the Kalman filter 604 or thecontroller 601, or according to any other intermittent rate or schedule,for example. Generally speaking, in this case, the Kalman filter 604 andthe PID controller 601 execute on a periodic basis at a faster or a muchfaster rate than the rate at which the measurement update is received bythe Kalman filter 604 from the transmitter 606. To accommodate thissituation, the modified Kalman filter 604 includes a switch unit 662that receives a residual signal produced from the summer 418 andprovides that signal to a Kalman gain block 622. In particular, duringthe execution cycle at which a new value of the process measurementoutput signal is available at the interface 660, and during apredetermined number of execution cycles after the execution cycle atwhich a new value of the process measurement output signal is available,the switch unit 662 operates to provide a new value of the residual tothe Kalman gain block 622. The new value of the residual is determinedin the combiner 418 based on the a priori process state variableestimate {circumflex over (X)}_(j) ⁻ and a received value of the processmeasurement output signal at the interface 660. As such, during theexecution cycle at which a new value of the process measurement outputsignal is available, the received value of the process measurementoutput signal corresponds to the newly received value of the processmeasurement output signal. On the other hand, during execution cyclesafter the predetermined number of execution cycles after the executioncycle at which a new value of the process measurement output signal isavailable at the interface 660, the switch unit 662 operates to providea stored value of the residual to the Kalman gain block 622. Generallyspeaking, the stored value of the residual may be determined in thecombiner 418 during the last execution cycle of the predetermined numberof execution cycles, and stored in the switch unit 662. The purpose ofusing the predetermined number of execution cycles is to allow theprocess state variable {circumflex over (X)}_(j) ⁻ to be fully updatedwith respect to the newly received process measurement output signal tothereby determine a more accurate value of the stored residual. Thus,during execution cycles after the predetermined number of executioncycles at which a new value of the process measurement output signal isavailable, the Kalman filter 604 uses the stored value of the residualin the switch unit 662 to produce the correction signal used to producethe process variable estimate {circumflex over (X)}_(j) to be sent tothe controller 601.

Still further, during the execution cycle at which a new processmeasurement output value Z_(j) is transmitted to, received at orotherwise available at the interface 660, and during the predeterminednumber of execution cycles after the execution cycle at which a newprocess measurement output value Z_(j) is available, the Kalman gainblock 622 is configured to calculate a new value for the Kalman gainK_(j) for use in the Kalman gain block 622. However, during theexecution cycles after the predetermined number of execution cyclesafter the execution cycle at which a new process measurement outputvalue Z_(j) is available, the Kalman gain block 622 is configured to usea stored value of the Kalman gain K_(j), where the stored value of theKalman gain K_(j) is calculated during the last execution cycle of thepredetermined number of execution cycles. In addition, because thecovariance values R used in the calculation of the Kalman gain valuesare only calculated using measurement values that have been transmitted,the PID control algorithm 630 of the controller 601 still works using anewly predicted value of the process variable measurement.

More particularly, during operation, the interface 660 operates to storea newly received value of the sensed or measured process output signal4, as sensed and sent by the transmitter 606. The interface 660 providesthe value of this newly received and stored process output signal Z_(j)to the input of the summer or combiner 418 for use in producing theresidual on the line 440. Subsequently, the switch unit 662 operates toprovide the residual to the gain block 622. At the same time, theinterface 660 sets a new value flag whenever the interface 660 receivesa new value from the transmitter 606, and provides this new value flagto both the switch unit 662 and the gain block 622. When the new valueflag is set, the switch unit 662 clears any previously stored residualvalue. The switch unit 662 then operates to provide a newly determinedvalue of the residual, as determined, for example, by the summer 418when a new process output signal Z_(j) is received at the interface 660,to the Kalman gain block 622. The switch unit 662 continues to provide anew value of the residual to the Kalman gain block 622 during thepredetermined number of execution cycles. On the last execution cycle ofthe predetermined number of execution cycles, the switch unit 662proceeds to store the residual calculated during this last executioncycle. Thus, during the execution cycles after the predetermined numberof execution cycles, the switch unit 662 operates to provide the Kalmangain block 622 with the stored residual value. Likewise, when the newvalue flag is set, the Kalman gain block 622 clears any previouslystored Kalman gain value. Thus, during the execution cycle at which anew value of the process output signal Z_(j) has been received at theinterface 660 and for the predetermined number of execution cyclesthereafter, the Kalman gain block 622 proceeds to calculate a new Kalmangain K_(j) for use in the gain block 622. The Kalman gain block 622continues to calculate a new Kalman gain value for use in the gain block622 during the predetermined number of execution cycles. However, on thelast execution cycle of the predetermined number of execution cycles,the Kalman gain block 622 proceeds to store the Kalman gain K_(j)calculated during this last execution cycle. Accordingly, during theexecution cycles after the predetermined number of execution cycles, theKalman gain block 622 operates to use the stored Kalman gain K_(j).Consequently, the switch unit 662 and the Kalman gain block 622 operateduring each execution cycle of the Kalman filter 604 to generate anestimate of the noise, which is then provided to the summer 414 and usedto produce the estimated process state variable value {circumflex over(X)}_(j). In this manner, the Kalman filter 604 provides accurateprocess variable estimates during or between the times at which newprocess variable measurements are received at the Kalman filter 604. Ifdesired, the predetermined number of execution cycles is at least oneand maybe more, such as two, three, etc. As will be understood, thepurpose of storing and using a residual that has been calculated afteror at the end of one or more predetermined execution cycles after thereceipt of a new process variable measurement value is allow the storedresidual to be more accurate because it will based on a differencebetween the most recently received process variable measurement and anestimate of that measurement made using the most recently receivedprocess variable itself instead of an estimate based on a previouslyreceived process variable measurement. In particular, it will take oneor more execution cycles of the Kalman filter for the process statevariable to be calculated via the feedback path 423 using a processvariable estimate calculated from a residual determined using the newprocess variable measurement.

If the process noise having a non-zero mean value is present in theprocess 602, then the process model within the Kalman filter 604 needsto be modified in order to account for this noise. In particular, FIG. 7illustrates a process control system 700 that includes a controller 701(which may be similar to or identical to the controllers 601, 501, 401,301 and 201 and has a controller algorithm 730) coupled to a process702, which includes process noise with non-zero mean value. The controlsystem 700 includes a Kalman filter 704 modified as described above withrespect to FIG. 6 to receive wireless process variable measurements thatare provided in a non-periodic, intermittent or slow manner. As will beseen, the Kalman filter 704 is the same as the Kalman filter 604 of FIG.6 (and like elements are numbered the same), except that the Kalmanfilter 704 includes a gain block 722, a filter block 724, and a summer726, which are similar to and operate for the same purposes as the gainblock 522, the filter block 524, and the summer 526 of FIG. 5. Thus, inthis case, the Kalman filter 704 calculates a compensated process statevariable estimate {tilde over (X)}_(j) that does not include any offsetcaused by the process noise with non-zero mean value. Of course, in thiscase, the filter 722 uses the same residual as used by the filter 622,i.e., either a newly calculated residual (during the execution cycle atwhich a new process measurement value is received and the predeterminednumber of execution cycles thereafter), or a stored residual (during theexecution cycles after the predetermined number of execution cyclesafter which a new process variable measurement value has been received).

As will be understood, the modified Kalman filters 604 and 704 of FIGS.6 and 7 enable the use of intermittently, non-periodically or slowlyprovided process variable measurement values provided from a processvia, for example, a wireless transmission network. As a result, theKalman filters 604 and 704 enable Kalman filter based control in processsituations in which process variable values are measured or are sent tothe control system in a non-periodic or intermittent manner, or at arate that is slower or less than the execution rate of the Kalman filterand the controller.

When using a Kalman filter, such as those of FIGS. 6 and 7, it isdesirable or sometimes necessary to configure the model parameters, suchas the parameters a, b and h described above. For example, in aself-regulating process, the model parameters may be set based onknowledge of the process gain, the process time constant and the processdead time. For an integrating process, the model parameters may be setbased on knowledge of process integrating gain and the process deadtime. To minimize the need for the user to set these parameters, themodel used in the Kalman filter could be automatically configured basedon the controller tuning parameter and some assumptions about thetuning. For example, if the control algorithm is a PID algorithm, thenthe Kalman filter model parameters could be set based on the PIDcontroller gain, reset and rate.

When the Kalman filter is used in wireless control, then thecalculations associated with the Kalman gain may be simplified if thenoise level is constant or insignificant. For example, if the noisecovariance R, associated with measurement noise is assumed to be zero(0), then the Kalman gain is a constant and may be calculated ordetermined as:

${K_{j} = \frac{1}{h}},{{{assuming}\mspace{14mu} R} = 0.}$

FIG. 8 is provided to illustrate the performance of a control systemusing the modified Kalman filter described above which receivedintermittent process variable measurements, as compared to traditionalPID based control systems that receive wired or periodic measurements.In particular, FIG. 8 depicts a chart 800 illustrating a computersimulated operation of a control system such as that of FIG. 7 having aPID controller and a modified Kalman filter operating as described withrespect to FIGS. 6 and 7 (in which process variable measurements areprovided to the input of the Kalman filter at a rate less than theexecution rate of the control system) as compared to PID control, inwhich periodic process variable measurements are provided to the inputof the PID controller at a rate greater than or equal to the executionrate of the control system. In FIG. 8, the line 801 indicates thesetpoint value provided to the controller while the line 802 indicatesthe value of an unmeasured disturbance in the process being controlled.As will be seen, each of these variables are changed at different timesto illustrate the response of and operation of both types of controlsystems to each of these two types of changes during process operation.

The lines 810 and 811 of FIG. 8 illustrate the simulated value of theprocess variable being controlled (PV) by the typical control system(PID controller with periodic measurement feedback) and the modifiedcontrol system (PID controller with modified Kalman filter that receivesnon-periodic, e.g., wireless, measurement feedback and compensates fornon-zero mean value noise), respectively. Likewise, the lines 820 and821 illustrate the simulated value of the control signal output by thePID controller by the typical control system (PID controller withperiodic measurement feedback) and the control signal output by the PIDcontroller of the modified control system (PID controller with modifiedKalman filter that receives non-periodic, e.g., wireless, measurementfeedback and compensates for non-zero mean value noise), respectively.These lines demonstrate how a modified Kalman filter may be used with aPID controller to provide closed loop control using a wirelessmeasurement as compared to a PID controller with a wired transmitter.

In this simulated test, the process used for comparison was a firstorder plus dead time process with the following characteristics:

Process Gain=1

Process Time Constant=6 sec

Process Dead Time=2 sec

The PID controller in all cases was tuned for a lambda factor of 1.

GAIN=1/Process Gain

RESET=Process Time Constant+Process Dead Time

The process input and output were scaled to 0-100% to make comparisonseasier to graph. Thus, the value of h (the unit conversion factor) wasequal to one (1) in these examples. For the modified Kalman filter, thenoise level was minimal and thus the Kalman filter gain was set to aconstant value of 1/h=1. The simulated wireless transmitter wasconfigured for 1 percent change and 10 second default period usingwindowed communications. The module (controller) execution rate was setat 0.5 seconds.

As will be seen from a close inspection of the lines 810 and 811 and thelines 820 and 821, the modified control system using a Kalman filteroperated very similarly to a PID control system in which periodicprocess variable measurements are provided to the controller at a rategreater than or equal to the execution rate of the control system. Infact, as illustrated in the chart of FIG. 8, the control performance wascomparable to a PID controller and a wired measurement for both setpointchanges and for large unmeasured process disturbances. As part of thetest module, the integrated absolute error (IAE) of control withwireless and the IAE for control with a wired measurement werecalculated as 361 and 336 respectively, thus evidencing nearly identicalor very comparable control performance on this measure.

FIG. 9 illustrates one manner of implementing a modified predictor and acontroller within a control system 1000, such as the control system ofFIG. 1. As illustrated in FIG. 9, a controller 1001 is connected tocontrol a process 1002 and a modified predictor 1004 is communicativelycoupled between the controller 1001 and a wireless transmitter 1006,which measures one or more process variables within the process 1002.The modified predictor 1004 may be any of the modified predictorsdescribed above, or a modified predictor constructed based on thetechniques disclosed herein. As illustrated in FIG. 9, the modifiedpredictor 1004 is configured as, and is operated or executed as aseparate and stand-alone block, such as a separate function block or aseparate control module associated with the control system 1000. Forexample, where the controller 1001 is implemented as a function block orcontrol module, then the modified predictor 1004 could be implemented asa separate function block or a separate control module that iscommunicatively coupled to the controller function block or module asillustrated in FIG. 9. In this case, a control block 1030 of thecontroller 1001 may be stored and executed within a separate device fromthe predictor 1004 (in which case device to device communications occurfor the communications between the predictor 1004 and the control block1030 of the controller 1001) or may be stored and executed within thesame device as the predictor 1004 (in which case intra-devicecommunications may occur for the communications between the predictor1004 and the control block 1030 of the controller 1001). In either case,these two blocks are communicatively coupled to one another via acommunication line, path or network.

In this example, the modified predictor block 1004 can becommunicatively connected to the control block 1030 in a manner suchthat the block 1004 provides an updated process variable estimate to thecontroller block 1030 at least once per execution cycle of thecontroller 1001. In any case, the modified predictor block 1004 could belocated in various different ones of the controller, the field devices,the I/O devices, etc. of FIG. 1 while the controller 1001 can be locatedin the same or other such devices.

FIG. 10 depicts a process control system 1100 in which a controllerblock 1101 is used to control a process 1102 and a modified predictorblock 1104 receives wireless transmission signals from a wirelesstransmitter 1106. Here, however, the modified predictor block 1104 islocated in the same composite block, but in a separate module, as thecontroller algorithm block 1130. Thus, in this example, the controlleralgorithm block 1130 and the modified predictor 1104 are integratedtogether in the same composite module (indicated as a composite block1140) and thus execute in the same device in the process controlnetwork, such as the process control network 10 of FIG. 1. Typically,this module would be executed in one of the controllers 11, but could beany of the other devices of FIG. 1, including wired or wireless fielddevices, I/O devices, etc. In this case, the user interface provided forthe controller block 1101 may show the predicted measurement valueproduced by the predictor 1104 as the process variable PV measurementinput into the controller block 1101. In most cases, however, the plantoperator would be more interested in viewing the last communicatedmeasurement value when accessing or viewing the control operation. Toallow the measurement value to be shown to the operator as the controlparameter, a controller block that contains the controller and modifiedpredictor blocks could be created as illustrated in, for example, FIG.11.

In particular, FIG. 11 illustrates an example of a control block 1201 inwhich a predictor block 1204 (indicated in FIG. 11 to be a modifiedKalman filter) is integrated into the same block as a controlleralgorithm block 1230. Thus, in this case, the control block or module1201 includes both the controller algorithm function block 1230 and themodified Kalman filter block 1204. In this case, the modified Kalmanfilter block 1204 is treated as part of the control module 1201 itselfand is thus viewable (and has parameters thereof accessible) as part ofthe control block 1201. Thus, in this situation, the process measurementoutput Z would appear to the operator to be the feedback processvariable (PV) input to the controller 1201.

While the discussion provided herein has presumed that, for each of thedifferent described embodiments, the controller coupled to the modifiedpredictor implements a PID control routine, this description is providedfor the sake of consistency only. It will be understood that other typesof control algorithms besides traditional PID control algorithms (whichinclude any form of PID, such as P, PI, PD, PID, etc.) may be used asthe controller in a control scheme using a modified predictor asdescribed herein. Of course there are many other ways of implementingthe modified predictors described herein, and as will be understood, themodified predictors may be used together with (e.g., in the same moduleor device) or separately from (e.g., in different modules or devices) asthe controllers or the control blocks or control elements to which themodified predictors are connected. Likewise, the controllers and themodified predictors described herein may be implemented in hardware, insoftware routines executed on a general purpose computer, or in softwareor firmware routines executed on a specific purpose computer orprocessor device.

In any of the disclosed embodiments, the modified predictors or thedevices in which the modified predictors may include a communicationsstack to process the incoming process variable measurement signals, anda module or routine to detect when an incoming signal has provided ameasurement update. The detection routine may then generate a flag orother signal to denote that data being provided via the communicationsstack includes a new measurement or other type of value or update. Thenew data and the update flag may then be provided to one or moreelements of the predictors as described above to be implemented asdiscussed above in connection with the operation of the observer andcontrol routines. Alternatively, or in addition, the new data and theupdate flag may be provided to one or more monitoring modules orapplications executed in, for example, the controller 11 of FIG. 1 orelsewhere in the control system. The update detection functionality maybe implemented at the function block level as well, and may be providedby one or more function blocks associated with a control and/or observermodule. Other wireless devices, such as the field device 71, may includesimilar components and functionality to support the reception andprocessing of such signals by, for instance, one or more of the functionblocks (e.g., FB1 and FB2) resident therein.

In some cases, the communications stack and the update detection modulesmay be implemented by one or more of the I/O devices 26, 28, 73 and 74of FIG. 1. Furthermore, the manner in which the update detection modulemakes its determination may involve hardware, software, firmware or anycombination thereof, and may involve any suitable routine for comparingvalues of the process variable.

The process control systems described herein may be utilized inconnection with communication schemes, such as wireless communications,involving process control data transmissions made on areport-by-exception basis. Exception reporting of the process controldata in a wireless communication context may present a number ofadvantages. For example, the rate at which power is consumed in thefield by the transmitters or other field devices may be lowered, therebyconserving battery power or other limited power supplies. Unlike pastexception reporting, however, the disclosed techniques support thetransmission of data utilized in a process control routine executed on aperiodic basis. Despite the admonitions of the past discouraging theexecution of process control routines utilizing data provided on anevent-triggered basis, practice of the disclosed techniques accommodatesthe periodic execution of process control routines without detrimentalsacrifices in performance. The disclosed techniques further supportproviding data to system monitoring applications on an event-triggeredbasis, similarly without detrimental sacrifices in performance

Although well suited for, and described at times herein in connectionwith, wireless communication schemes, practice of the disclosedtechniques is not limited to any particular communication scheme,context, or protocol, or any process control network, architecture,controller or system, or any monitoring application. Instead, thedisclosed techniques may be applied in any number or variety of contextsin which process control data is transmitted less frequently than thecontrol routine execution period, or monitoring cycle, and for anydesired reason. Such contexts may present undesirable or adverseconditions making communications unreliable or intermittent.Accordingly, the foregoing description is set forth with theunderstanding that practice of the disclosed techniques is not limitedto the low-power or other wireless communication schemes described inparticular herein.

As will be understood, the communication techniques described above forthe wireless (or other) transmitters of FIGS. 1, 3, 6, 7 and 9-11generally result in non-periodic, irregular or otherwise less frequentdata transmissions when the communication of measurement values from thefield to the controller 11 has traditionally been structured to reportin a periodic manner to, in turn, support the periodic execution of thecontrol routine(s). In other words, the control routines are generallydesigned for, and rely on, periodic updates of the measurement values.To accommodate the non-periodic or otherwise unavailable measurementupdates (and other unavailable communication transmissions), thepredictor, control and monitoring routine(s) may be restructured ormodified as described above to enable the process control system 10 torely on non-periodic or other intermittent updates that occur lessfrequently than the control execution period or some other standardperiod. In this manner, the disclosed techniques may, in some cases,generally support a form of exception reporting for the process variablemeasurements despite the periodic execution of the process controlroutines. The disclosed techniques may also address and support a formof exception reporting involving transmissions between the controlroutine and the devices downstream of the control routine, e.g., theactuators and other devices or elements responsive to the control signalgenerated by the control routine.

Of course, it will be understood that, in some cases, devices maycollect and monitor multiple different measurements (i.e., measurementsof different signals) and that the same transmission techniquesdescribed herein could be used for each of these one or moremeasurements. Still further, in some cases, the devices collecting themeasurements can perform an advanced analysis (such as an errordetection analysis, tuning analysis, etc.) or measurement on the dataand the same communication techniques described herein could be appliedto determine whether or not to send a full analysis, a single status, orwait until the next sample interval to transmit a signal.

In accordance with some aspects of the disclosure, the techniquesdescribed herein may be applied in contexts in which a number ofdifferent wireless (or other) communications between the controller andthe field device(s) or other elements of the process control system areundesirably delayed or lost. Accordingly, the foregoing examplesregarding communication problems between the controller and atransmitter, and between a controller and an actuator, have been setforth with the understanding that they are exemplary in nature.Furthermore, the parameters involved in the communications are notlimited to process variables being controlled by the control routine. Tothe contrary, the disclosed techniques may be applied in connection withcommunications involving any parameter being measured or fed back orotherwise communicated for use by the control routine or a monitoringroutine. As a result, the response indications described above (i.e., aprocess variable measurement and an actuator position) are set forthwith the understanding that they are exemplary in nature. Communicationproblems involving other data indicative of a response to the controlsignal may also be addressed by the disclosed techniques. As a result,any communication of data from an element (e.g., field device, anotherprocess control routine, etc.) downstream of the control routine may beinvolved.

Practice of the disclosed methods, system and techniques is not limitedto any one particular wireless architecture or communication protocol.In fact, the disclosed modifications to the control routines arewell-suited for any context in which the control routine is implementedin a periodic manner, but without process variable measurement updatesfor each control iteration. Other exemplary contexts include where asampled value is provided irregularly or more seldom by, for instance,an analyzer or via lab samples.

Moreover, practice of the disclosed technique is not limited to use withsingle-input, single-output PI or PID control routines, but rather maybe applied in a number of different multiple-input and/ormultiple-output control schemes and cascaded control schemes that usepredictors. More generally, the disclosed technique may also be appliedin the context of any closed-loop model-based control routine involvingone or more process variables, one or process inputs or other controlsignals, such as model predictive control (MPC).

The term “field device” is used herein in a broad sense to include anumber of devices or combinations of devices (i.e., devices providingmultiple functions, such as a transmitter/actuator hybrid), as well asany other device(s) that perform(s) a function in a control system. Inany event, field devices may include, for example, input devices (e.g.,devices such as sensors and instruments that provide status, measurementor other signals that are indicative of process control parameters suchas, for example, temperature, pressure, flow rate, etc.), as well ascontrol operators or actuators that perform actions in response tocommands received from controllers and/or other field devices.

When implemented, any of the units, blocks, modules, switches,combiners, adders, gain blocks, etc. described herein may executed assoftware or firmware that is stored in any computer readable memory suchas on a magnetic disk, a laser disk, or other storage medium, in a RAMor ROM of a computer or processor, etc. Thus, the specific hardware likeembodiments described herein can be implemented in software on acomputer processor using the techniques described herein. Likewise, thissoftware may be delivered to a user, a process plant or an operatorworkstation using any known or desired delivery method including, forexample, on a computer readable disk or other transportable computerstorage mechanism or over a communication channel such as a telephoneline, the Internet, the World Wide Web, any other local area network orwide area network, etc.

While the present invention has been described with reference tospecific examples, which are intended to be illustrative only and not tobe limiting of the invention, it may be apparent to those of ordinaryskill in the art that changes, additions or deletions may be made to thedisclosed embodiments without departing from the spirit and scope of theinvention.

The invention claimed is:
 1. A control system for use in controlling a process, the control system comprising: a control unit including a process variable input and a control routine unit communicatively coupled to the process variable input, wherein the control routine unit generates a control signal for use in controlling the process based on a process variable value received at the process variable input; a Kalman filter unit coupled to the control unit, the Kalman filter unit operating once during each of a number of execution cycles to produce a process variable estimate, the Kalman filter unit including; a control signal input coupled to receive the control signal produced by the control routine unit, an interface including a process variable feedback input that receives a process variable measurement signal less frequently than once per the execution cycle time of the Kalman filter unit, a process model coupled to receive the control signal at the control signal input to produce an initial process variable estimate, a correction unit coupled to use the process variable measurement signal received via the process variable feedback input to produce a correction signal from a residual, wherein the correction unit includes a first combiner, a switch unit and a gain unit, and a second combiner coupled to the process model and to the correction unit to combine the initial process variable estimate with the correction signal to produce a further process variable estimate; wherein during the execution cycle of the Kalman filter unit at which a new value of the process variable measurement signal is available and during a predetermined number of execution cycles after the execution cycle at which a new value of the process variable measurement signal is available, the switch unit operates to provide a new value of the residual to the gain unit to produce the correction signal, wherein the new value of the residual is determined by combining the initial process variable estimate with a value of the process variable measurement signal at the first combiner; wherein during execution cycles of the Kalman filter unit after the predetermined number of execution cycles after the execution cycle at which a new value of the process variable measurement signal is available, the switch unit operates to provide a stored value of the residual to the gain unit to produce the correction signal, wherein the stored value of the residual is determined during the one of the execution cycles of the predetermined number of execution cycles; and wherein the process variable input of the control unit is coupled to receive the process variable estimate based on the further process variable estimate.
 2. The control system of claim 1, wherein the process variable estimate is the further process variable estimate.
 3. The control system of claim 1, wherein the gain unit multiplies a value of the residual by a gain value to produce the correction signal; wherein during the execution cycle of the Kalman filter unit at which a new value of the process variable measurement signal is available and during the predetermined number of execution cycles after the execution cycle at which a new value of the process variable measurement is available, the gain unit operates to determine a new gain value for use in the gain unit; and wherein during the execution cycles of the Kalman filter unit after the predetermined number of execution cycles after the execution cycle at which a new value of the process variable measurement signal is available, the gain unit operates to use a stored gain value, wherein the stored gain value is determined during one of the execution cycles of the predetermined number of executions cycles.
 4. The control system of claim 3, wherein the gain unit is a Kalman gain unit that determines a Kalman gain value for use in the gain unit.
 5. The control system of claim 1, wherein the Kalman filter unit further includes; a compensation unit coupled to use the residual to produce a compensation signal, wherein the compensation unit includes a further gain unit that multiples a value of the residual by a further gain value and a filter unit that receives the value of the residual multiplied by the further gain value to produce the compensation signal, and a third combiner coupled to the second combiner and to the compensation unit to combine the further process variable estimate with the compensation signal to produce the process variable estimate.
 6. The control system of claim 5, wherein the further gain unit is a Kalman gain unit that determines a value of one minus a Kalman gain value for use in the further gain unit.
 7. The control system of claim 5, wherein the filter unit is a first order filter.
 8. The control system of claim 1, wherein the control routine unit stores and implements a proportional-integral-derivative control algorithm to produce the control signal.
 9. A method of controlling a process, comprising: implementing, on a computer processor device, a control routine during each of a number of execution cycles to produce a control signal for use in controlling the process; receiving, at a computer processor device, a process variable measurement signal less frequently than the execution cycle time; implementing, on a computer processor device, a Kalman filter routine during each of the number of execution cycles to produce the process variable estimate, including; receiving the control signal produced by the control routine during each of the number of execution cycles, producing an initial process variable estimate using a process model to model the reaction of the process based on the control signal during each of the number of execution cycles, producing a correction signal from a residual during each of the number of execution cycles, combining, during each of the number of execution cycles, the initial process variable estimate with the correction signal to produce a further process variable estimate, and producing a process variable estimate based on the further process variable estimate, wherein during the execution cycle at which a newly received value of the process variable measurement signal is available and during a predetermined number of execution cycles after the execution cycle at which a newly received value of the process variable measurement is available, producing the correction signal from the residual includes using a new value of the residual, wherein the new value of the residual is determined by combining the initial process variable estimate with a received value of the process variable measurement signal, and wherein during execution cycles after the predetermined number of execution cycles after the execution cycle at which a newly received value of the process variable measurement signal is available, producing the correction signal from the residual includes using a stored value of the residual, wherein the stored value of the residual is determined during one of the execution cycles of the predetermined number of execution cycles.
 10. The method of claim 9, wherein the process variable estimate is the further process variable estimate.
 11. The method of claim 9, wherein producing the correction signal from the residual includes determining a gain value and multiplying a value of the residual by the gain value to produce the correction signal during each of the number of execution cycles, and determining a new gain value for use during the execution cycle at which a new value of the process variable measurement signal is available and during the predetermined number of execution cycles after the execution cycle at which a new value of the process variable measurement signal is available, and using a stored gain value during the execution cycles after the predetermined number of execution cycles after the execution cycle at which a new value of the process variable measurement signal is available.
 12. The method of claim 11, wherein determining the gain value includes determining a Kalman gain value.
 13. The method of claim 9, wherein implementing the Kalman filter routine further includes, determining a compensation signal during each of the number of execution cycles, and producing, during each of the number of execution cycles, the process variable estimate by combining the further process variable estimate with the compensation signal.
 14. The method of claim 13, wherein determining the compensation signal includes determining a further gain value and multiplying a value of the residual by the further gain value and filtering the value of the residual multiplied by the further gain value to produce the compensation signal during each of the number of execution cycles.
 15. The method of claim 14, wherein determining the further gain value includes determining a value of one minus a Kalman gain value.
 16. The method of claim 14, wherein filtering the value of the residual multiplied by the further gain value includes using a first order filtering. 